145 citations · 145 across the 3 of their papers we have counts for
3 papers
Scaling Laws for Differentially Private Language Models
Ryan McKenna, Yangsibo Huang, Amer Sinha +9
Scaling laws have emerged as important components of large language model (LLM) training as they can predict performance gains through scale, and provide guidance on important hype…
Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards
Yangsibo Huang, Milad Nasr, Anastasios Angelopoulos +10
It is now common to evaluate Large Language Models (LLMs) by having humans manually vote to evaluate model outputs, in contrast to typical benchmarks that evaluate knowledge or ski…
Gemma 2: Improving Open Language Models at a Practical Size
Gemma Team, Morgane Riviere, Shreya Pathak +195
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In th…